| Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques G Qin, Y Wei, L Yu, J Xu, J Ojih, AD Rodriguez, H Wang, Z Qin, M Hu Journal of Materials Chemistry A 11 (11), 5801-5810, 2023 | 18 | 2023 |
| Machine learning accelerated discovery of promising thermal energy storage materials with high heat capacity J Ojih, U Onyekpe, A Rodriguez, J Hu, C Peng, M Hu ACS applied materials & interfaces 14 (38), 43277-43289, 2022 | 14 | 2022 |
| Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network J Ojih, A Rodriguez, J Hu, M Hu Energy and AI 14, 100286, 2023 | 10 | 2023 |
| Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations J Ojih, M Al-Fahdi, AD Rodriguez, K Choudhary, M Hu npj Computational Materials 8 (1), 143, 2022 | 10 | 2022 |
| Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage J Ojih, M Al-Fahdi, Y Yao, J Hu, M Hu Journal of Materials Chemistry A 12 (14), 8502-8515, 2024 | 3 | 2024 |
| Searching extreme mechanical properties using active machine learning and density functional theory J Ojih University of South Carolina, 2021 | 3 | 2021 |
| High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models J Ojih, C Shen, A Rodriguez, H Zhang, K Choudhary, M Hu Journal of Materials Chemistry A 11 (44), 24169-24183, 2023 | 2 | 2023 |
| Correction: Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage J Ojih, M Al-Fahdi, Y Yao, J Hu, M Hu Journal of Materials Chemistry A 12 (27), 16929-16929, 2024 | | 2024 |
| Energy and AI J Ojih, A Rodriguez, J Hu, M Hu | | |
Ming HuUniversity of South Carolina在 sc.edu 的电子邮件经过验证
